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We estimate the causal effect of each county in the U.S. on children's earnings and other outcomes in adulthood using a fixed effects model that is identified by analyzing families who move across counties with children of different ages. Using these estimates, we (a) quantify how much places matter for upward mobility, (b) construct predictions of the causal effect of growing up in each county that can be used to guide families seeking to move to opportunity, and (c) characterize which types of areas produce better outcomes. For children growing up in low-income families, each year of childhood exposure to a one standard deviation (SD) better county increases income in adulthood by 0.5%. Hence, growing up in a one SD better county from birth increases a child's income by approximately 10%. There is substantial local area variation in children's outcomes: for example, growing up in the western suburbs of Chicago (DuPage county) would increase a given child's earnings by 30% relative to growing up in Cook county. Counties with less concentrated poverty, less income inequality, better schools, a larger share of two-parent families, and lower crime rates tend to produce greater upward mobility. Boys' outcomes vary more across areas than girls, and boys have especially poor outcomes in highly segregated areas. One-fifth of the black-white earnings gap can be explained by differences in the counties in which black and white children grow up. Areas that generate better outcomes tend to have higher house prices, but our approach uncovers many "opportunity bargains" - places that generate good outcomes but are not very expensive.

Zonder onderwerpscode: economie

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Value-added (VA) models measure the productivity of agents such as teachers or doctors based on the outcomes they produce. The utility of VA models for performance evaluation depends on the extent to which VA estimates are biased by selection, for instance by differences in the abilities of students assigned to teachers. One widely used approach for evaluating bias in VA is to test for balance in lagged values of the outcome, based on the intuition that today's inputs cannot influence yesterday's outcomes. We use Monte Carlo simulations to show that, unlike in conventional treatment effect analyses, tests for balance using lagged outcomes do not provide robust information about the degree of bias in value-added models for two reasons. First, the treatment itself (value-added) is estimated, rather than exogenously observed. As a result, correlated shocks to outcomes can induce correlations between current VA estimates and lagged outcomes that are sensitive to model specification. Second, in most VA applications, estimation error does not vanish asymptotically because sample sizes per teacher (or principal, manager, etc.) remain small, making balance tests sensitive to the specification of the error structure even in large datasets. We conclude that bias in VA models is better evaluated using techniques that are less sensitive to model specification, such as randomized experiments, rather than using lagged outcomes.

Zonder onderwerpscode: economie

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We show that the neighborhoods in which children grow up shape their earnings, college attendance rates, and fertility and marriage patterns by studying more than seven million families who move across commuting zones and counties in the U.S. Exploiting variation in the age of children when families move, we find that neighborhoods have significant childhood exposure effects: the outcomes of children whose families move to a better neighborhood - as measured by the outcomes of children already living there - improve linearly in proportion to the amount of time they spend growing up in that area, at a rate of approximately 4% per year of exposure. We distinguish the causal effects of neighborhoods from confounding factors by comparing the outcomes of siblings within families, studying moves triggered by displacement shocks, and exploiting sharp variation in predicted place effects across birth cohorts, genders, and quantiles to implement overidentification tests. The findings show that neighborhoods affect intergenerational mobility primarily through childhood exposure, helping reconcile conflicting results in the prior literature.